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A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations

Nature Biotechnology volume 19, pages 45–50 (2001)Cite this article

Abstract

A large proportion of the 6,000 genes present in the genome of Saccharomyces cerevisiae, and of those sequenced in other organisms, encode proteins of unknown function. Many of these genes are “silent,” that is, they show no overt phenotype, in terms of growth rate or other fluxes, when they are deleted from the genome. We demonstrate how the intracellular concentrations of metabolites can reveal phenotypes for proteins active in metabolic regulation. Quantification of the change of several metabolite concentrations relative to the concentration change of one selected metabolite can reveal the site of action, in the metabolic network, of a silent gene. In the same way, comprehensive analyses of metabolite concentrations in mutants, providing “metabolic snapshots,” can reveal functions when snapshots from strains deleted for unstudied genes are compared to those deleted for known genes. This approach to functional analysis, using comparative metabolomics, we call FANCY—an abbreviation for functional analysis by co-responses in yeast.

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Figure 1: Competition between FY23pfk27Δ and its wild-type parent.
Figure 2: Cluster analysis of NMR spectra from cell extracts.

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Acknowledgements

This work was supported by EC contracts, within the frame of the EUROFAN program, to S.G.O., K.v.D., and H.V.W., and by a grant from the UK's Biotechnology and Biological Sciences Research Council to S.G.O. and D.B.K. We would like to thank Cathy Day for her superb technical assistance, and Barbara Bakker and Johann Rohwer for stimulating discussions.

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Author notes

  1. Bas Teusink

    Present address: TNO-Prevention and Health, Zernikedreef 9, NL-2333 CK, Leiden, The Netherlands

  2. Michael C. Walsh

    Present address: Heineken Technical Services B.V., P.O. Box 510, NL-2380 BB, Zoeterwoude, The Netherlands

Authors and Affiliations

  1. Swammerdam Institute for Life Sciences, BioCentrum Amsterdam, University of Amsterdam, Plantage Muidergracht 12 , TV, NL-1018, Amsterdam, The Netherlands

    Léonie M. Raamsdonk, Bas Teusink, Michael C. Walsh, Jan A. Berden, Hans V. Westerhoff & Karel van Dam

  2. Institute of Biological Sciences, Cledwyn Building, University of Wales, Aberystwyth, SY23 3DD, Aberystwyth, UK

    David Broadhurst & Douglas B. Kell

  3. School of Biological Sciences, University of Manchester, 2.205 Stopford Building, Oxford Road, Manchester , M13 9PT, UK

    Nianshu Zhang, Andrew Hayes & Stephen G. Oliver

  4. Department of Biochemistry, University of Cambridge Old Addenbrooke's Site, 80 Tennis Court Road, Cambridge, CB2 1GA, UK

    Kevin M. Brindle

  5. Department of Computer Science, University of Wales, Aberystwyth, SY23 3DB, Aberystwyth, UK

    Jem J. Rowland

  6. Department of Molecular Cell Physiology, BioCentrum Amsterdam, Free University, De Boelelaan 1087, HV, NL-1081, Amsterdam, The Netherlands

    Hans V. Westerhoff

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Correspondence to Stephen G. Oliver.

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Raamsdonk, L., Teusink, B., Broadhurst, D. et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 19, 45–50 (2001). https://doi.org/10.1038/83496

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